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merging for efficient semantic segmentation. In: Proceedings of the 21st International Conference on Computer Vision Theory and Applications (VISAPP), pp. 43–54. (2026). [2] Sassoon, J., Szczepanski, M
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: Master's (M2) or engineering-school student in machine learning, applied mathematics or computer science Strong linear algebra; probability and optimization are a plus Solid Python and PyTorch Care in
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compression and its co-optimization with memory technology characteristics and constraints. You will work within CEA-Leti and CEA-List institutes, benefiting from a forefront ecosystem for AI model
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/Computer Vision pour analyser les images radioscopiques d'un mélange triphasique. Stage M2 de traitement et d'analyse d'images issues de radioscopies X de mélanges triphasiques. Mise en œuvre de méthodes de
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gaussian representation meets perception models for bev segmentation. In : 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. p. 2250-2259. [3] KERBL, Bernhard, KOPANAS
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workspace learning,” in European Conference on Computer Vision, 2026. [3] A. Brazi, B. Meden, F. M. de Chamisso, S. Bourgeois, and V. Lepetit, “Corr2distrib: Making ambiguous correspondences an ally to
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for High-Performance Automotive ADAS & Autonomous Driving, Product Folder. [7] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," IEEE Conference on Computer Vision and
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!), used in future quantum computer or spatial radio astronomy applications This internship provides an opportunity to improve our understanding of reliability physics at cryogenic temperature, and to offer
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qualifications master student or equivalent (2nd/3rd engineering school year) in computer science and/or machine learning; some knowledge of the Python programming language; ability to work in a team
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Physics, Applied Mathematics, or Computer Science, with experience in deep generative models (VAE, diffusion) and Python/PyTorch. Applications from statistical physicists are welcome. STILL HESITATING